This notebook contains a set of analyses for analyzing TomVasel’s boardgamegeek collection. The bulk of the analysis is focused on building a user-specific predictive model to predict the games that the specified user is likely to own. This enables us to ask questions like, based on the games the user currently owns, what games are a good fit for their collection? What upcoming games are they likely to purchase?
We can look at a basic description of the number of games that the user owns, has rated, has previously owned, etc.
What years has the user owned/rated games from? While we can’t see when a user added or removed a game from their collection, we can look at their collection by the years in which their games were published.
We can look at the most frequent types of categories, mechanics, designers, and artists that appear in a user’s collection.
We’ll examine predictive models trained on a user’s collection for games published through 2020. How many games has the user owned/rated/played in the training set (games prior to 2020)?
username | dataset | period | games_owned | games_rated |
TomVasel | training | published before 2020 | 1,385 | 5,178 |
TomVasel | validation | published 2020 | 0 | 368 |
TomVasel | test | published after 2020 | 0 | 283 |
The main outcome we will be modeling for the user is owned, which refers to whether the user currently owns or has a previously owned a game in their collection. Our goal is to train a predictive model to learn the probability that a user will add a game to their collection based on its observable features. This amounts to looking at historical data and looking to find patterns that exist between features of games and games present in the user’s collection.
One of the models we trained was a decision tree, which looks for decision rules that can be used to separate games the user owns from games they don’t. The resulting model produces a decision corresponding to yes or no statements: to explain why the model predicts the user to own game, we start at the top of the tree and follow the rules that were learned from the training data.
Note: the tree below has been further pruned to make it easier to visualize.
Decision trees are highly interpretible models that are easy to train and can identify important interactions and nonlinearities present in the data. Individual trees have the drawback of being less predictive than other common models, but it can be useful to look at them to gain some understanding of key predictors and relationships found in the training data.
We can examine coefficients from another model we trained, which is a logistic regression with elastic net regularization (which I will refer to as a penalized logistic regression). Positive values indicate that a feature increases a user’s probability of owning/rating a game, while negative values indicate a feature decreases the probability. To be precise, the coefficients indicate the effect of a particular feature on the log-odds of a user owning a game.
Why did the model identify these features? We can make density plots of the important features for predicting whether the user owned a game. Blue indicates the density for games owned by the user, while grey indicates the density for games not owned by the user.
Binary predictors can be difficult to see with this visualization, so we can also directly examine the percentage of games in a user’s collection with a predictor vs the percentage of all games with that predictor.
% of Games with Feature | ||||
username | Feature | User_Collection | All_Games | Ratio |
TomVasel | Rio Grande Games | 9.5% | 1.4% | 6.95 |
TomVasel | Artist John Kovalic | 2.5% | 0.4% | 6.86 |
TomVasel | Mayfair Games | 5.5% | 0.8% | 6.70 |
TomVasel | Fantasy Flight Games | 5.1% | 0.9% | 5.58 |
TomVasel | ZMan Games | 5.5% | 1.2% | 4.69 |
TomVasel | Eaglegryphon Games | 2.8% | 0.6% | 4.52 |
TomVasel | Gamewright | 2.2% | 0.7% | 3.36 |
TomVasel | Hasbro | 7.1% | 2.7% | 2.66 |
TomVasel | Asmodee | 5.9% | 2.4% | 2.44 |
TomVasel | Hand Management | 28.0% | 19.7% | 1.42 |
TomVasel | GMT Games | 1.1% | 1.4% | 0.80 |
TomVasel | Cooperative Game | 3.0% | 6.5% | 0.45 |
TomVasel | Wargame | 7.1% | 19.5% | 0.36 |
TomVasel | Crowdfunding Kickstarter | 4.7% | 13.2% | 0.35 |
TomVasel | Hexagon Grid | 3.0% | 12.3% | 0.24 |
TomVasel | Solitaire Only Games | 0.0% | 1.6% | 0.00 |
Before predicting games in upcoming years, we can examine how well the model did and what games it liked in the training set. In this case, we used resampling techniques (cross validation) to ensure that the model had not seen a game before making its predictions.
Displaying the 100 games from the training set with the highest probability of ownership, highlighting in blue games the user has owned.
Rank | Published | ID | Name | Pr(Owned) | Owned |
1 | 1997 | 42 | Tigris & Euphrates | 0.976 | no |
2 | 2001 | 878 | Wyatt Earp | 0.912 | no |
3 | 1800 | 45 | Perudo | 0.871 | no |
4 | 1988 | 550 | Barbarossa | 0.870 | yes |
5 | 2000 | 478 | Citadels | 0.869 | yes |
6 | 2010 | 20437 | Lords of Vegas | 0.848 | yes |
7 | 1994 | 199 | Manhattan | 0.836 | yes |
8 | 2013 | 143693 | Glass Road | 0.828 | no |
9 | 1994 | 398 | Wildlife Safari | 0.828 | yes |
10 | 2009 | 54643 | Skyline 3000 | 0.827 | no |
11 | 2000 | 495 | Time Pirates | 0.821 | no |
12 | 2004 | 9220 | Saboteur | 0.821 | no |
13 | 2012 | 129622 | Love Letter | 0.809 | no |
14 | 1999 | 125 | Money! | 0.803 | no |
15 | 1959 | 7688 | Memory | 0.797 | yes |
16 | 2000 | 822 | Carcassonne | 0.786 | yes |
17 | 2003 | 6263 | King's Breakfast | 0.784 | no |
18 | 2002 | 4636 | Clans | 0.783 | no |
19 | 1998 | 944 | Mag·Blast | 0.781 | no |
20 | 2007 | 20436 | Stonehenge: An Anthology Board Game | 0.760 | no |
21 | 1996 | 486 | Barnyard Buddies | 0.756 | no |
22 | 2002 | 5867 | 10 Days in Europe | 0.755 | yes |
23 | 2000 | 475 | Taj Mahal | 0.743 | yes |
24 | 2006 | 25417 | BattleLore | 0.739 | yes |
25 | 2003 | 7866 | 10 Days in the USA | 0.738 | yes |
26 | 2002 | 4390 | Carcassonne: Hunters and Gatherers | 0.737 | yes |
27 | 2000 | 986 | Babel | 0.734 | yes |
28 | 1998 | 3 | Samurai | 0.733 | yes |
29 | 1995 | 112 | Condottiere | 0.732 | yes |
30 | 2006 | 23142 | Mag·Blast: Third Edition | 0.727 | no |
31 | 2007 | 27588 | Zooloretto | 0.725 | yes |
32 | 1999 | 17 | Button Men | 0.715 | no |
33 | 2005 | 17710 | Conquest of the Empire | 0.714 | yes |
34 | 1999 | 632 | Cloud 9 | 0.712 | yes |
35 | 2005 | 15512 | Diamant | 0.712 | no |
36 | 2004 | 12942 | No Thanks! | 0.706 | yes |
37 | 2008 | 39463 | Cosmic Encounter | 0.705 | yes |
38 | 2004 | 10547 | Betrayal at House on the Hill | 0.702 | yes |
39 | 1948 | 320 | Scrabble | 0.700 | yes |
40 | 1958 | 2318 | Scrabble Junior | 0.696 | no |
41 | 2004 | 9216 | Goa | 0.690 | no |
42 | 2015 | 176920 | Mission: Red Planet (Second Edition) | 0.690 | no |
43 | 2004 | 13285 | Dungeonville | 0.685 | yes |
44 | 2010 | 65200 | Asteroyds | 0.681 | yes |
45 | 2010 | 77130 | Sid Meier's Civilization: The Board Game | 0.673 | no |
46 | 2000 | 638 | Hera and Zeus | 0.669 | no |
47 | 2002 | 3955 | BANG! | 0.667 | yes |
48 | 2010 | 68182 | Isla Dorada | 0.667 | no |
49 | 2008 | 35677 | Le Havre | 0.667 | yes |
50 | 1970 | 8392 | Buckaroo! | 0.662 | no |
51 | 2003 | 7865 | 10 Days in Africa | 0.652 | yes |
52 | 2003 | 5767 | Mammoth Hunters | 0.652 | yes |
53 | 2008 | 34635 | Stone Age | 0.651 | yes |
54 | 1967 | 3656 | Score Four | 0.639 | no |
55 | 2000 | 556 | 7 Safari | 0.637 | no |
56 | 1991 | 19 | Wacky Wacky West | 0.636 | no |
57 | 2009 | 42207 | Super Circles | 0.634 | no |
58 | 1977 | 2593 | Pass the Pigs | 0.625 | yes |
59 | 1956 | 2243 | Yahtzee | 0.621 | yes |
60 | 1982 | 170 | Family Business | 0.620 | yes |
61 | 1964 | 7709 | Hands Down | 0.620 | yes |
62 | 2007 | 27173 | Vikings | 0.619 | yes |
63 | 1995 | 929 | The Great Dalmuti | 0.616 | no |
64 | 1963 | 2679 | Mouse Trap | 0.615 | yes |
65 | 2000 | 823 | The Lord of the Rings | 0.614 | no |
66 | 2003 | 8203 | Hey, That's My Fish! | 0.613 | yes |
67 | 2006 | 22399 | MixUp | 0.609 | no |
68 | 2007 | 22398 | 10 Days in Asia | 0.603 | yes |
69 | 2004 | 9027 | Oasis | 0.601 | yes |
70 | 2013 | 133956 | Axis & Allies: WWI 1914 | 0.600 | no |
71 | 2005 | 15880 | The Hollywood! Card Game | 0.599 | no |
72 | 2000 | 854 | Doge | 0.598 | no |
73 | 1998 | 503 | Through the Desert | 0.597 | no |
74 | 2016 | 205398 | Citadels | 0.595 | no |
75 | 2000 | 826 | Cartagena | 0.594 | yes |
76 | 1999 | 51 | Ricochet Robots | 0.592 | yes |
77 | 2007 | 27976 | Heroscape Master Set: Swarm of the Marro | 0.586 | no |
78 | 2009 | 41010 | Carcassonne Junior | 0.585 | yes |
79 | 2004 | 9439 | FBI | 0.583 | no |
80 | 2009 | 55253 | Atlantis | 0.580 | yes |
81 | 1995 | 915 | Mystery of the Abbey | 0.579 | no |
82 | 2007 | 31481 | Galaxy Trucker | 0.579 | yes |
83 | 2018 | 257501 | KeyForge: Call of the Archons | 0.578 | no |
84 | 2001 | 63539 | Lupus in Tabula | 0.576 | no |
85 | 1999 | 403 | Elk Fest | 0.576 | yes |
86 | 2010 | 64956 | 10 Days in the Americas | 0.573 | yes |
87 | 1992 | 118 | Modern Art | 0.571 | yes |
88 | 2001 | 1927 | Munchkin | 0.571 | yes |
89 | 2013 | 135557 | Wizard's Brew | 0.570 | no |
90 | 1997 | 256 | Mississippi Queen | 0.569 | no |
91 | 1946 | 1917 | Stratego | 0.569 | yes |
92 | 2010 | 54361 | Heroscape Master Set: Battle for the Underdark | 0.568 | no |
93 | 1998 | 49 | Mamma Mia! | 0.565 | yes |
94 | 2002 | 3972 | Catan: Portable Edition | 0.565 | no |
95 | 1968 | 7972 | Crocodile Pool Party | 0.564 | yes |
96 | 1999 | 595 | Escape from Elba | 0.559 | no |
97 | 1988 | 466 | Inkognito | 0.555 | yes |
98 | 1994 | 18 | RoboRally | 0.554 | yes |
99 | 2011 | 100423 | Elder Sign | 0.553 | no |
100 | 2015 | 177639 | Raptor | 0.551 | no |
This section contains a variety of visualizations and metrics for assessing the performance of the model(s) during resampling. If you’re not particularly interested in predictive modeling, skip down further to the predictions from the model.
An easy way to examine the performance of classification model is to view a separation plot. We plot the predicted probabilities from the model for every game (from resampling) from lowest to highest. We then overlay a blue line for any game that the user does own. A good classifier is one that is able to separate the blue (games owned by the user) from the white (games not owned by the user), with most of the blue occurring at the highest probabilities (right side of the chart).
We can more formally assess how well each model did in resampling by looking at the area under the receiver operating characteristic curve. A perfect model would receive a score of 1, while a model that cannot predict the outcome will default to a score of 0.5. The extent to which something is a good score depends on the setting, but generally anything in the .8 to .9 range is very good while the .7 to .8 range is perfectly acceptable.
wflow_id | .metric | .estimator | .estimate |
Decision Tree | roc_auc | binary | 0.81 |
GLM | roc_auc | binary | 0.79 |
Another way to think about the model performance is to view its lift, or its ability to detect the positive outcomes over that of a null model. High lift indicates the model can much more quickly find all of the positive outcomes (in this case, games owned or played by the user), while a model with no lift is no better than random guessing. A gains chart is another way to view this.
While we are probably more interested in the lift provided by the models to evaluate their efficacy, we can also explore the optimal cutpoint if we wanted to define a hard threshold for identifying games a user will own vs not own.
The threshold we select depends on how we much we care about false positives (games the model predicts that the user does not own) vs false negatives (games the user owns that the model does not predict). We can toggle threshold to
Finally, we can understand the performance of the model by examining its calibration. If the model assigns a probability of 5%, how often does the outcome actually occur? A well calibrated model is one in which the predicted probabilities reflect the probabilities we would observe in the actual data. We can assess the calibration of a model by grouping its predictions into bins and assessing how often we observe the outcome versus how often our model expects to observe the outcome.
A model that is well calibrated will closely follow the dashed line - its expected probabilities match that of the observed probabilities. A model that consistently underestimates the probability of the event will be over this dashed line, be a while a model that overestimates the probability will be under the dashed line.
What games does the model think TomVasel is most likely to own that are not in their collection?
Published | ID | Name | Pr(Owned) | Owned |
1997 | 42 | Tigris & Euphrates | 0.976 | no |
2001 | 878 | Wyatt Earp | 0.912 | no |
1800 | 45 | Perudo | 0.871 | no |
2013 | 143693 | Glass Road | 0.828 | no |
2009 | 54643 | Skyline 3000 | 0.827 | no |
What games does the model think TomVasel is least likely to own that are in their collection?
Published | ID | Name | Pr(Owned) | Owned |
2014 | 164928 | Orléans | 0.010 | yes |
2012 | 119506 | Freedom: The Underground Railroad | 0.010 | yes |
2013 | 145103 | 7 Days of Westerplatte | 0.011 | yes |
1976 | 670 | Starship Troopers | 0.011 | yes |
2014 | 141572 | Paperback | 0.012 | yes |
Top 25 games most likely to be owned by the user in each year, highlighting in blue the games that the user has owned.
rank | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 |
1 | Love Letter | Glass Road | Patchwork | Mission: Red Planet (Second Edition) | Citadels | Cartagena | KeyForge: Call of the Archons | KeyForge: Worlds Collide |
2 | Android: Netrunner | Axis & Allies: WWI 1914 | Splendor | Raptor | Star Wars: Destiny | Miaui | Cosmic Encounter: 42nd Anniversary Edition | Era: Medieval Age |
3 | Axis & Allies 1941 | Wizard's Brew | Roll for the Galaxy | A Game of Thrones: The Card Game (Second Edition) | Heir to the Pharaoh | Pandemic Legacy: Season 2 | Ultimate Werewolf Legacy | Munchkin Warhammer 40,000 |
4 | Wiz-War (Eighth Edition) | Asgard's Chosen | Temporum | Celestia | Thunder & Lightning | SpyNet | New Frontiers | Star Wars: Outer Rim |
5 | The Last Banquet | Tash-Kalar: Arena of Legends | Ultimate Werewolf | Smash Up: Munchkin | Agricola: Family Edition | Fallout | Underwater Cities | KeyForge: Age of Ascension |
6 | Descent: Journeys in the Dark (Second Edition) | Nosferatu | Akrotiri | My First Bohnanza | Chariot Race | Yamataï | Munchkin Collectible Card Game | Robin of Locksley |
7 | Cowtown | La Boca | DungeonQuest Revised Edition | Catan: Traveler – Compact Edition | Agricola (Revised Edition) | Legend of the Five Rings: The Card Game | Beta Colony | Queenz: To Bee or Not to Bee |
8 | Mafia: Vendetta | BattleLore: Second Edition | Catan: Ancient Egypt | Flea Market | New Angeles | Werwölfe | Arkham Horror (Third Edition) | Pandemic: Rapid Response |
9 | Star Trek: Catan | Rococo | Saboteur: The Duel | Fury of Dracula (Third/Fourth Edition) | Star Wars: Rebellion | Junggle | Fae | Monster Baby Rescue! |
10 | Zooloretto: The Dice Game | Caverna: The Cave Farmers | Ca$h 'n Guns (Second Edition) | Hengist | Magic: The Gathering – Arena of the Planeswalkers: Shadows over Innistrad | Wasteland Express Delivery Service | Heroes of Terrinoth | Unmatched Game System |
11 | Rex: Final Days of an Empire | BANG! The Dice Game | Warhammer 40,000: Conquest | Harald | Mansions of Madness: Second Edition | Smile | Star Wars: X-Wing (Second Edition) | Unmatched: Battle of Legends, Volume One |
12 | Urbania | Blood Bound | Five Tribes | Mogul | Game of Thrones: The Iron Throne | Trash Pandas | Carcassonne: Safari | Hadara |
13 | 10 Tage durch Deutschland | City of Remnants | Antike II | Isle of Skye: From Chieftain to King | Hit Z Road | Jump Drive | Ticket to Ride: New York | Sushi Roll |
14 | Africana | Asante | Qwixx Card Game | Oh My Goods! | Cthulhu in the House | Coal Country | History of the World | One Night Ultimate Super Villains |
15 | Carcassonne: Winter Edition | Knuckle Sammich: A Kobolds Ate My Baby! Card Game | Munchkin Treasure Hunt | Magic: The Gathering – Arena of the Planeswalkers | DOOM: The Board Game | Food Chain | One Week Ultimate Werewolf | Ticket to Ride: London |
16 | Aeroplanes: Aviation Ascendant | Sushi Go! | Age of War | Through the Ages: A New Story of Civilization | Arkham Horror: The Card Game | Riverboat | Pandemic: Fall of Rome | Nanty Narking |
17 | Pick-a-Dog | Forbidden Desert | Star Wars: Imperial Assault | King Chocolate | Bohnanza: The Duel | Bärenpark | Les Aventuriers du Rail Express | Football Highlights 2052 |
18 | Ginkgopolis | Renaissance Man | Fields of Arle | Pandemic Legacy: Season 1 | A Feast for Odin | Best of Werewolves of Miller's Hollow | Zoinx! | Suburbia: Collector's Edition |
19 | Clash of Wills: Shiloh 1862 | Bruges | Murano | XCOM: The Board Game | For Crown & Kingdom | Majesty: For the Realm | The Way of the Bear | Butterfly |
20 | Tzolk'in: The Mayan Calendar | Munchkin Pathfinder | Munchkin Panic | Grand Austria Hotel | Fight for Olympus | Oktoberfest | The River | Pictures |
21 | Zug um Zug: Deutschland | Bruxelles 1893 | The Staufer Dynasty | Forbidden Stars | Smash Up: It's Your Fault! | Werewords | Duelosaur Island | Love Letter |
22 | Munchkin Conan | Cinque Terre | Outfoxed! | Elfenroads | Sushi Go Party! | Munchkin Shakespeare Deluxe | Titan Dice | Silver |
23 | Clash of Cultures | Road Rally USA | Roll Through the Ages: The Iron Age | Arboretum | Quadropolis | Twilight Imperium: Fourth Edition | Broadhorns: Early Trade on the Mississippi | MegaCity: Oceania |
24 | Android: Infiltration | Cube Quest | Mad City | Imagine | A Game of Thrones: Hand of the King | Ticket to Ride: Germany | Race to the New Found Land | Machi Koro Legacy |
25 | Rocket Jockey | Zen Garden | Pandemic: Contagion | One Night Revolution | Power Grid: The Card Game | Ticket to Ride: First Journey (Europe) | Forbidden Sky | Unmatched: Robin Hood vs. Bigfoot |
This is an interactive table for the model’s predictions for the training set (from resampling).
We’ll validate the model by looking at its predictions for games published in 2020. That is, how well did a model trained on a user’s collection through 2020 perform in predicting games for the user in 2020?
username | outcome | dataset | method | .metric | .estimate |
TomVasel | owned | validation | Decision Tree | roc_auc | |
TomVasel | owned | validation | GLM | roc_auc |
Table of top 50 games from 2020, highlighting games that the user owns.
Published | ID | Name | Pr(Owned) | Owned |
2020 | 298572 | Cosmic Encounter Duel | 0.413 | no |
2020 | 301607 | KeyForge: Mass Mutation | 0.312 | no |
2020 | 314040 | Pandemic Legacy: Season 0 | 0.262 | no |
2020 | 317985 | Beyond the Sun | 0.246 | no |
2020 | 294232 | Stolen Paintings | 0.234 | no |
2020 | 309105 | Sagani | 0.190 | no |
2020 | 294231 | Gangster's Dilemma | 0.188 | no |
2020 | 302267 | Hi Lo Flip | 0.188 | no |
2020 | 269810 | Nevada City | 0.188 | no |
2020 | 184267 | On Mars | 0.187 | no |
2020 | 299939 | Doodle Dungeon | 0.175 | no |
2020 | 304285 | Infinity Gauntlet: A Love Letter Game | 0.174 | no |
2020 | 309630 | Small World of Warcraft | 0.170 | no |
2020 | 309000 | Blue Skies | 0.164 | no |
2020 | 296100 | Rococo: Deluxe Edition | 0.157 | no |
2020 | 301919 | Pandemic: Hot Zone – North America | 0.155 | no |
2020 | 309113 | Ticket to Ride: Amsterdam | 0.149 | no |
2020 | 325635 | Unmatched: Little Red Riding Hood vs. Beowulf | 0.149 | no |
2020 | 294216 | Musical Chairs | 0.141 | no |
2020 | 293141 | King of Tokyo: Dark Edition | 0.139 | no |
2020 | 293889 | Fallout Shelter: The Board Game | 0.132 | no |
2020 | 316377 | 7 Wonders (Second Edition) | 0.131 | no |
2020 | 302270 | Marshmallow Test | 0.129 | no |
2020 | 320819 | Dinner in Paris | 0.127 | no |
2020 | 257001 | Munchkin Dungeon | 0.126 | no |
2020 | 284777 | Unmatched: Jurassic Park – InGen vs Raptors | 0.120 | no |
2020 | 302260 | Abandon All Artichokes | 0.120 | no |
2020 | 294484 | Unmatched: Cobble & Fog | 0.117 | no |
2020 | 302926 | Silver Coin | 0.117 | no |
2020 | 302809 | Betrayal at Mystery Mansion | 0.116 | no |
2020 | 318098 | Silver Dagger | 0.115 | no |
2020 | 315060 | Unmatched: Buffy the Vampire Slayer | 0.114 | no |
2020 | 308765 | Praga Caput Regni | 0.113 | no |
2020 | 218948 | Apocalypse Road | 0.113 | no |
2020 | 316927 | Sweet Existence: A Strange Planet Card Game | 0.111 | no |
2020 | 318983 | Faiyum | 0.111 | no |
2020 | 295577 | Dungeon Mayhem: Monster Madness | 0.106 | no |
2020 | 265784 | Cleopatra and the Society of Architects: Deluxe Edition | 0.105 | no |
2020 | 299169 | Spicy | 0.104 | no |
2020 | 300877 | New York Zoo | 0.103 | no |
2020 | 317105 | Tiny Epic Galaxies BLAST OFF! | 0.102 | no |
2020 | 300322 | Hallertau | 0.097 | no |
2020 | 302310 | Nanaki | 0.097 | no |
2020 | 297661 | Gold River | 0.095 | no |
2020 | 295486 | My City | 0.093 | no |
2020 | 245658 | Unicorn Fever | 0.092 | no |
2020 | 303552 | Magic: The Gathering – Unsanctioned | 0.089 | no |
2020 | 246900 | Eclipse: Second Dawn for the Galaxy | 0.089 | no |
2020 | 300010 | Dragomino | 0.086 | no |
2020 | 327797 | Monopoly Bid | 0.084 | no |
We can then refit our model to the training and validation set in order to predict all upcoming games for the user.
Examine the top 100 upcoming games, highlighting in blue ones the user already owns.
Published | ID | Name | Pr(Owned) | Owned |
2021 | 316080 | KeyForge: Dark Tidings | 0.278 | no |
2021 | 344408 | Full Throttle! | 0.209 | no |
2021 | 329841 | Ticket to Ride: Europe – 15th Anniversary | 0.182 | no |
2021 | 340466 | Unfathomable | 0.180 | no |
2021 | 342848 | World of Warcraft: Wrath of the Lich King | 0.176 | no |
2021 | 342073 | Berried Treasure | 0.165 | no |
2022 | 335764 | Unmatched: Battle of Legends, Volume Two | 0.153 | no |
2021 | 330608 | Cryo | 0.137 | no |
2021 | 341009 | Armonia | 0.134 | no |
2021 | 329670 | Pandemic: Hot Zone – Europe | 0.134 | no |
2021 | 341048 | Free Ride | 0.132 | no |
2022 | 288080 | Dice Realms | 0.130 | no |
2021 | 238799 | Messina 1347 | 0.126 | no |
2022 | 353470 | Star Wars: Jabba's Palace – A Love Letter Game | 0.122 | no |
2022 | 237179 | Weather Machine | 0.120 | no |
2021 | 331635 | Kameloot | 0.114 | no |
2022 | 344268 | The Mother Road: Route 66 | 0.113 | no |
2021 | 322708 | Descent: Legends of the Dark | 0.111 | no |
2021 | 297531 | Watch | 0.111 | no |
2021 | 319793 | Happy City | 0.110 | no |
2021 | 339906 | The Hunger | 0.109 | no |
2021 | 318709 | For Sale Autorama | 0.104 | no |
2021 | 311920 | Ultimate Werewolf: Extreme | 0.100 | no |
2021 | 308566 | Nova Lux | 0.099 | no |
2021 | 262941 | Dominant Species: Marine | 0.095 | no |
2021 | 299659 | Clash of Cultures: Monumental Edition | 0.089 | no |
2021 | 326848 | Illumination | 0.088 | no |
2022 | 349067 | The Lord of the Rings: The Card Game – Revised Core Set | 0.087 | no |
2021 | 332420 | Nexum: Galaxy | 0.087 | no |
2021 | 318996 | Welcome to Sysifus Corp | 0.085 | no |
2021 | 315937 | X-Men: Mutant Insurrection | 0.083 | no |
2021 | 325349 | Risky Chicken | 0.082 | no |
2021 | 336794 | Galaxy Trucker | 0.080 | no |
2022 | 346199 | A Game of Thrones: B'Twixt | 0.079 | no |
2021 | 329529 | Magellan: Elcano | 0.079 | no |
2021 | 329450 | Equinox | 0.078 | no |
2022 | 326945 | Castles of Mad King Ludwig: Collector's Edition | 0.078 | no |
2021 | 329593 | Settlement | 0.078 | no |
2021 | 328479 | Living Forest | 0.078 | no |
2022 | 342177 | Word Heist | 0.077 | no |
2021 | 337262 | Fangs | 0.075 | no |
2021 | 337397 | Warhammer Underworlds: Two-Player Starter Set | 0.075 | no |
2021 | 341530 | Super Mega Lucky Box | 0.073 | no |
2021 | 329714 | Dreadful Circus | 0.073 | no |
2021 | 301257 | Maglev Metro | 0.073 | no |
2021 | 262477 | Mercado de Lisboa | 0.073 | no |
2021 | 334782 | Bayou Bash | 0.071 | no |
2021 | 341286 | Eriantys | 0.070 | no |
2022 | 299594 | Megapulse | 0.069 | no |
2021 | 346624 | 신묘한 사다리 (Mysterious Ladder) | 0.067 | no |
2021 | 329607 | Baseball Highlights: The Dice Game | 0.066 | no |
2021 | 314491 | Meadow | 0.066 | no |
2021 | 261246 | Tiny Ninjas Heroes | 0.066 | no |
2022 | 348463 | ECO: Coral Reef | 0.066 | no |
2021 | 331946 | Faux Diamonds | 0.065 | no |
2021 | 322014 | All-Star Draft | 0.063 | no |
2021 | 259962 | Stress Botics | 0.061 | no |
2021 | 334644 | Nicodemus | 0.061 | no |
2021 | 348461 | Castle Break | 0.061 | no |
2021 | 283387 | Rocketmen | 0.060 | no |
2021 | 316287 | Quest | 0.060 | no |
2022 | 349793 | Age of Rome | 0.059 | no |
2021 | 333144 | Stronghold: Undead (Second Edition) | 0.059 | no |
2021 | 339790 | Cocktail | 0.059 | no |
2021 | 328478 | Dungeons & Dragons: Dungeon Scrawlers – Heroes of Undermountain | 0.059 | no |
2021 | 332944 | Sobek: 2 Players | 0.058 | no |
2021 | 333348 | Dirge: The Rust Wars | 0.058 | no |
2021 | 326804 | Rorschach | 0.057 | no |
2021 | 266448 | Imperium: The Contention | 0.057 | no |
2021 | 330665 | Bellum Magica | 0.056 | no |
2021 | 346603 | Hungry Little Demons | 0.056 | no |
2021 | 327062 | Popcorn Dice | 0.056 | no |
2021 | 331685 | Hit the Silk! | 0.055 | no |
2021 | 300664 | Arkwright: The Card Game | 0.055 | no |
2021 | 313841 | Lunar Base | 0.054 | no |
2021 | 221298 | NewSpeak | 0.054 | no |
2021 | 281248 | Cape May | 0.054 | no |
2021 | 340040 | Neoville | 0.054 | no |
2022 | 331106 | The Witcher: Old World | 0.054 | no |
2021 | 288254 | Simplicity | 0.053 | no |
2021 | 325853 | L.A.M.A. Dice | 0.053 | no |
2021 | 303676 | Oh My Brain | 0.052 | no |
2021 | 333136 | Football Highlights: The Dice Game | 0.052 | no |
2021 | 299684 | Khôra: Rise of an Empire | 0.052 | no |
2021 | 337389 | Snakesss | 0.052 | no |
2022 | 314745 | Now or Never | 0.052 | no |
2021 | 304324 | Dive | 0.052 | no |
2021 | 339789 | Welcome to the Moon | 0.052 | no |
2022 | 344050 | Dubious | 0.051 | no |
2021 | 306321 | Night of the Ninja | 0.051 | no |
2022 | 342927 | Fire & Stone | 0.051 | no |
2022 | 351605 | Bohnanza: 25th Anniversary Edition | 0.051 | no |
2022 | 292509 | The Shadow Planet: The Board Game | 0.051 | no |
2022 | 305462 | The Age of Atlantis | 0.051 | no |
2021 | 344114 | Bag of Chips | 0.051 | no |
2021 | 329084 | Space Dragons | 0.050 | no |
2021 | 298069 | Cubitos | 0.050 | no |
2021 | 348065 | Nicaea | 0.049 | no |
2021 | 338980 | Eastern Empires | 0.049 | no |
2021 | 332386 | Brew | 0.049 | no |